class_docstr: Text Processor class implement NER, POS tag, lexical tree parsing.
  EnProcessor is designed by single instance mode.
docstr: Dependency parsing based on spacy model.
  
  Example::
  
      EnProcessor().get_dep_parser(
      'The quick brown fox jumps over the lazy dog.'
      )
  
      >>
          The     DT      4       det
          quick   JJ      4       amod
          brown   JJ      4       amod
          fox     NN      5       nsubj
          jumps   VBZ     0       root
          over    IN      9       case
          the     DT      9       det
          lazy    JJ      9       amod
          dog     NN      5       obl
  
  :param str|list sentence: input text string
  :param bool is_one_sent: whether do sentence tokenzie
  :param bool split_by_space: whether tokenize sentence by split with " "
  :return: dp tags.
docstr_tok: ["Dependency","parsing","based","on","spacy","model",".","Example",":",":","EnProcessor","(",")",".get_dep_parser","(","'The","quick","brown","fox","jumps","over","the","lazy","dog",".","'",")",">",">","The","DT","4","det","quick","JJ","4","amod","brown","JJ","4","amod","fox","NN","5","nsubj","jumps","VBZ","0","root","over","IN","9","case","the","DT","9","det","lazy","JJ","9","amod","dog","NN","5","obl",":","param","str|list","sentence",":","input","text","string",":","param","bool","is_one_sent",":","whether","do","sentence","tokenzie",":","param","bool","split_by_space",":","whether","tokenize","sentence","by","split","with","``","``",":","return",":","dp","tags","."]
code: def get_dep_parser(
      self, sentence, is_one_sent=True, split_by_space=False
  ):
      r"""
      Dependency parsing based on spacy model.
  
      Example::
  
          EnProcessor().get_dep_parser(
          'The quick brown fox jumps over the lazy dog.'
          )
  
          >>
              The	DT	4	det
              quick	JJ	4	amod
              brown	JJ	4	amod
              fox	NN	5	nsubj
              jumps	VBZ	0	root
              over	IN	9	case
              the	DT	9	det
              lazy	JJ	9	amod
              dog	NN	5	obl
  
      :param str|list sentence: input text string
      :param bool is_one_sent: whether do sentence tokenzie
      :param bool split_by_space: whether tokenize sentence by split with " "
      :return: dp tags.
  
      """
      self.check_initialized()
  
      if self.__dp_parser is None:
          self.__dp_parser = self.nlp.pipeline[2][1]
  
      if self.__word2vec is None:
          self.__word2vec = self.nlp.pipeline[0][1]
  
      if self.__pos_tag is None:
          self.__pos_tag = self.nlp.pipeline[1][1]
  
      if isinstance(sentence, list):
          tokens = sentence
      elif isinstance(sentence, str):
          # list of tokens
          tokens = self.tokenize(sentence, is_one_sent, split_by_space)
      else:
          raise ValueError(
              "Support string or token list input, "
              "while your input type is {0}".format(type(sentence))
          )
  
      tokens = self.__pos_tag(
          self.__word2vec(Doc(self.nlp.vocab, words=tokens))
      )
      parse = self.__dp_parser(tokens)
  
      return [
          (
              word.text,
              word.tag_,
              word.head.i + 1 if word.dep_ != "ROOT" else 0,
              word.dep_,
          )
          for word in parse
      ]
  
imports: import re
  import nltk
  import spacy
  import threading
  from functools import reduce
  from spacy.tokens import Doc
  from .nltk_res_load import *
  from ..settings import *
label_desc: Use image node_id 13 for calling the EnProcessor obj's underlying member method code with example usage: obj.get_dep_parser(sentence, is_one_sent, split_by_space) and returns: unknown